Symbols Without Context

Abstract

Humans have invaluable real-world knowledge because we have accumulated a lifetime of experiences that help us recognize, understand, and anticipate. Computers do not have real-world experiences to guide them, so they must rely on statistical patterns in their digital data base—which may be helpful, but is certainly fallible. We use emotions as well as logic to construct concepts that help us understand what we see and hear. When we see a dog, we may visualize other dogs, think about the similarities and differences between dogs and cats, or expect the dog to chase after a cat we see nearby. We may remember a childhood pet or recall past encounters with dogs. Remembering that dogs are friendly and loyal, we might smile and want to pet the dog or throw a stick for the dog to fetch. Remembering once being scared by an aggressive dog, we might pull back to a safe distance. A computer does none of this. For a computer, there is no meaningful difference between dog, tiger, and XyB3c, other than the fact that they use different symbols. A computer can count the number of times the word dog is used in a story and retrieve facts about dogs (such as how many legs they have), but computers do not understand words the way humans do, and will not respond to the word dog the way humans do. The lack of real world knowledge is often revealed in software that attempts to interpret words and images. Language translation software programs are designed to convert sentences written or spoken in one language into equivalent sentences in another language. In the 1950s, a Georgetown–IBM team demonstrated the machine translation of 60 sentences from Russian to English using a 250-word vocabulary and six grammatical rules. The lead scientist predicted that, with a larger vocabulary and more rules, translation programs would be perfected in three to five years. Little did he know! He had far too much faith in computers. It has now been more than 60 years and, while translation software is impressive, it is far from perfect. The stumbling blocks are instructive. Humans translate passages by thinking about the content—what the author means—and then expressing that content in another language.